Network intrusion prediction using associative classification method

Research output: Contribution to journalArticle

Abstract

The increasing rates of intrusion in the network systems has placed many organizations and v; institutions in a very vulnerable position of having to face risks which could involve great financial or losses. A large number of computers have been hacked over the years because some business organiz; and other concerns had not taken the essential precautionary measures to protect their networks cyber-attacks. Since, the frequency of the attacks on the network systems have dramatically increas number and in the level of destruction over the past years, the Intrusion Detection System has becoi important component to ensure the safety for such network systems. This study uses the assoc classification algorithm to predict oncoming network intrusions. In order to protect network syster algorithm is used to detect a variety of relationships and association rules of interest in the flow of 1 incidents. For this research, an attempted network thread data was obtained from a finance compa Malaysia, comprising more than 38,000 records and 20 attributes. The association rule mining is us discover the sequence of the incidents that enables the model to predict the forth coming incidents network systems which in this case are considered as intrusions.

Original languageEnglish
Pages (from-to)153-159
Number of pages7
JournalAsian Journal of Information Technology
Volume11
Issue number5
DOIs
Publication statusPublished - 2012

Fingerprint

prediction
finance
safety
method
rate
attribute
loss
detection

Keywords

  • Association rule mining
  • Malaysia
  • Network intrusion
  • Organizations
  • Sequential incidents

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Network intrusion prediction using associative classification method. / Ahmad Nazri, Mohd Zakree; Abdul Majid, Nor Emizan; Abu Bakar, Azuraliza; Mohd Sarim, Hafiz.

In: Asian Journal of Information Technology, Vol. 11, No. 5, 2012, p. 153-159.

Research output: Contribution to journalArticle

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